under the License.
-->

<project>
  <modelVersion>4.0.0</modelVersion>
  <artifactId>foo</artifactId>
  <groupId>bar</groupId>
  <version>1.0</version>
  <packaging>pack</packaging>
  <build>
    <plugins>
      <plugin>
        <artifactId>maven-it-plugin</artifactId>
        <version></version>
      </plugin>
    </plugins>
  </build>

// A ParallelDevice on its own is not registered with a TFE_Context, and so has
// no device name (e.g. for `tf.device`). `NamedParallelDevice` associates a
// name with it, which lets us pack its `ParallelTensor`s into TFE_TensorHandles
// placed on the parallel device.
class NamedParallelDevice {
 public:
  NamedParallelDevice(const std::string& name,

	// Check if we can use USTAR prefix/suffix splitting.
	var namePrefix string
	if prefix, suffix, ok := splitUSTARPath(hdr.Name); ok {
		namePrefix, hdr.Name = prefix, suffix
	}

	// Pack the main header.
	var f formatter
	blk := tw.templateV7Plus(hdr, f.formatString, f.formatOctal)
	f.formatString(blk.toUSTAR().prefix(), namePrefix)
	blk.setFormat(FormatUSTAR)
	if f.err != nil {

        TFE_DeleteTensorHandle(outputs[i]);
      }
    }
    return status.status;
  }

  absl::Status Pack(absl::Span<ImmediateExecutionTensorHandle*> handles,
                    ImmediateExecutionTensorHandle** result) override {
    TF_Status status;
    *result = tensorflow::unwrap(device_.pack(context_,
                                              tensorflow::wrap(handles.data()),

      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =
          tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(
              unwrapped_handle);
      tensorflow::ImmediateExecutionTensorHandle* result;
      status->status = custom_device_handle->device()->Pack(

1 !important;\n  flex-shrink: 1 !important;\n}\n\n.justify-content-start {\n  -ms-flex-pack: start !important;\n  justify-content: flex-start !important;\n}\n\n.justify-content-end {\n  -ms-flex-pack: end !important;\n  justify-content: flex-end !important;\n}\n\n.justify-content-center {\n  -ms-flex-pack: center !important;\n  justify-content: center !important;\n}\n\n.justify-content-between {\n  -ms-flex-pack: justify !important;\n  justify-content: space-between !important;\n}\n\n.justify-content-around...

  if (first_bad_status != nullptr) {
    TF_SetStatus(status, TF_GetCode(first_bad_status.get()),
                 TF_Message(first_bad_status.get()));
    return result;
  }
  // For each output of the original operation, pack the per-device
  // TensorHandles we've computed into a single parallel TensorHandle.
  std::vector<std::unique_ptr<ParallelTensor>> per_device_outputs;
  per_device_outputs.reserve(first_op_output_count);

  // Add a sync point to make sure that variables have been initialized
  // before the function execution starts.
  TFE_ContextAsyncWait(ctx, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Pack 3 variable handles into one TFE_TensorHandle.
  // When remote is false, function device is placed on task0. Handle types are
  // REMOTE, REMOTE, LOCAL on task0. When remote is true, function device is

.toast,\n.toast-top-full-width .toast {\n  max-width: inherit;\n}\n\n.pace {\n  z-index: 1048;\n}\n\n.pace .pace-progress {\n  z-index: 1049;\n}\n\n.pace .pace-activity {\n  z-index: 1050;\n}\n\n.pace-primary .pace .pace-progress {\n  background: #007bff;\n}\n\n.pace-barber-shop-primary .pace {\n  background: #fff;\n}\n\n.pace-barber-shop-primary .pace .pace-progress {\n  background: #007bff;\n}\n\n.pace-barber-shop-primary .pace .pace-activity {\n  background-image: linear-gradient(45deg, rgba(255, 255,...

`tf.reverse_v2()` will remain until 1.0 final. * `tf.mul`, `tf.sub` and `tf.neg`
are deprecated in favor of `tf.multiply`, `tf.subtract` and `tf.negative`. *
`tf.pack` and `tf.unpack` are deprecated in favor of `tf.stack` and
`tf.unstack`. * `TensorArray.pack` and `TensorArray.unpack` are getting
deprecated in favor of `TensorArray.stack` and `TensorArray.unstack`. * The